enable zerogpu

app.py

@@ -268,28 +268,48 @@ hands = mp_hands.Hands(
 #     latent = opts.latent_scaling_factor * autoencoder.encode(image[None, ...]).sample()
 #     return image[None, ...], latent
 
+# @spaces.GPU(duration=60)
+# def make_ref_cond(image):
+#     def initialize_and_process(image):
+#         os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+#         torch.cuda.set_device(0)
+#         print("Initializing autoencoder in worker process")
+#         image = image.to("cuda")
+#         latent = opts.latent_scaling_factor * autoencoder.encode(image[None, ...]).sample()
+#         return image[None, ...], latent
+
+#     from multiprocessing import Process, Queue
+#     queue = Queue()
+
+#     def worker(image, queue):
+#         result = initialize_and_process(image)
+#         queue.put(result)
+
+#     process = Process(target=worker, args=(image, queue))
+#     process.start()
+#     process.join()
+
+#     return queue.get()
+
 @spaces.GPU(duration=60)
 def make_ref_cond(image):
+    result = []
+
     def initialize_and_process(image):
         os.environ["CUDA_VISIBLE_DEVICES"] = "0"
         torch.cuda.set_device(0)
-        print("Initializing autoencoder in worker process")
+
+        print("Initializing autoencoder in worker thread")
         image = image.to("cuda")
         latent = opts.latent_scaling_factor * autoencoder.encode(image[None, ...]).sample()
-        return image[None, ...], latent
-
-    from multiprocessing import Process, Queue
-    queue = Queue()
-
-    def worker(image, queue):
-        result = initialize_and_process(image)
-        queue.put(result)
+        result.append((image[None, ...], latent))
 
-    process = Process(target=worker, args=(image, queue))
-    process.start()
-    process.join()
+    from threading import Thread
+    thread = Thread(target=initialize_and_process, args=(image,))
+    thread.start()
+    thread.join()
 
-    return queue.get()
+    return result[0]
 
 def get_ref_anno(ref):
     print("inside get_ref_anno")